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removing last checkpoint file

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Goekdeniz-Guelmez 2024-11-21 22:33:56 +01:00
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llms/mlx_lm/models/mamba2-working_but_giberish.py
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import math
from dataclasses import dataclass, field
from typing import Tuple, Union
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import MambaCache
@dataclass
class ModelArgs(BaseModelArgs):
num_heads: int
head_dim: int
vocab_size: int
hidden_size: int
state_size: int
num_hidden_layers: int
layer_norm_epsilon: float
expand: int
conv_kernel: int
n_groups: int
use_bias: bool
use_conv_bias: bool
initializer_range: float
residual_in_fp32: bool
time_step_min: float
time_step_max: float
time_step_floor: float
rescale_prenorm_residual: bool
rms_norm: bool
chunk_size: int
tie_word_embeddings: bool
use_cache: bool = True
time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
time_step_rank: Union[int, str] = "auto"
model_type: str = "mamba2"
def __post_init__(self):
if not hasattr(self, "intermediate_size"):
self.intermediate_size = int(self.expand * self.hidden_size)
if not hasattr(self, "head_dim"):
self.head_dim = self.hidden_size // self.num_heads
if self.time_step_rank == "auto":
self.time_step_rank = math.ceil(self.hidden_size / 16)
class MambaRMSNormGated(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = mx.ones((hidden_size,))
self.variance_epsilon = eps
def __call__(self, hidden_states, gate=None):
if gate is not None:
hidden_states = hidden_states * nn.silu(gate)
variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states
def silu(x):
return x * mx.sigmoid(x)
def ssd(x, A, B, C, chunk_size):
batch, seqlen, nheads, dim = x.shape
B = mx.expand_dims(B, axis=2)
C = mx.expand_dims(C, axis=2)
state = mx.zeros((batch, nheads, dim, B.shape[-1]))
outputs = []
for i in range(0, seqlen, chunk_size):
chunk = slice(i, min(i + chunk_size, seqlen))
dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
x_chunk = x[:, chunk] # [batch, chunk_size, nheads, dim]
x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1]) # [batch, nheads, dim, chunk_size]
B_chunk = B[:, chunk] # [batch, chunk_size, state_size]
dBx = mx.matmul(x_chunk, B_chunk) # [batch, nheads, dim, state_size]
state = state * mx.expand_dims(dA, axis=-1) + dBx
C_chunk = C[:, chunk] # [batch, chunk_size, state_size]
y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1])) # [batch, nheads, dim, chunk_size]
y = mx.transpose(y, [0, 3, 1, 2]) # [batch, chunk_size, nheads, dim]
outputs.append(y)
return mx.concatenate(outputs, axis=1), state
class DepthWiseConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.padding = padding
self.groups = groups if groups is not None else in_channels
assert in_channels == out_channels, "In and out channels must be same for depthwise convolution"
assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
self.weight = mx.random.normal((in_channels, 1, kernel_size))
self.bias = mx.zeros((out_channels,)) if bias else None
def __call__(self, x: mx.array, cache=None) -> mx.array:
B, L, C = x.shape
K = self.kernel_size
assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
if cache is not None:
# Access conv_state directly from cache[0]
if cache[0] is None:
cache[0] = mx.zeros((B, K-1, C))
x = mx.concatenate([cache[0], x], axis=1)
outputs = []
for c in range(C):
x_c = x[:, :, c]
x_c = mx.expand_dims(x_c, axis=1)
w_c = self.weight[c]
if w_c.ndim == 2:
w_c = mx.expand_dims(w_c, axis=0)
elif w_c.ndim == 1:
w_c = mx.expand_dims(mx.expand_dims(w_c, axis=0), axis=0)
y_c = mx.conv_general(
x_c,
w_c,
stride=1,
padding=0
)
if self.bias is not None:
y_c = y_c + self.bias[c]
y_c = mx.squeeze(y_c, axis=1)
outputs.append(y_c)
y = mx.stack(outputs, axis=-1)
# Update cache directly using cache[0]
if cache is not None:
cache[0] = x[:, -K+1:, :] if x.shape[1] >= K else x
return y
class Mamba2Block(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
conv_dim = args.intermediate_size + 2 * args.state_size
self.conv1d = DepthWiseConv1d(
in_channels=conv_dim,
out_channels=conv_dim,
kernel_size=args.conv_kernel,
groups=conv_dim,
bias=args.use_conv_bias,
padding=args.conv_kernel - 1
)
self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
if args.rescale_prenorm_residual:
layer_scale = math.sqrt(1.0 / args.num_hidden_layers)
self.out_proj.weight = self.out_proj.weight * layer_scale
def __call__(self, u: mx.array, cache=None):
batch_size, seq_len, dimension = u.shape
# Initialize the negative A matrix
A = -mx.exp(self.A_log)
# Process sequence in chunks if needed
outputs = []
current_cache = cache
for i in range(seq_len):
# Extract current token
current_input = u[:, i:i+1, :]
# Initialize cache states if needed
if current_cache[0] is None: # conv state
conv_dim = self.args.intermediate_size + 2 * self.args.state_size
current_cache[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
if current_cache[1] is None: # ssm state
current_cache[1] = mx.zeros((
batch_size,
self.args.num_heads,
self.args.head_dim,
self.args.state_size
))
# Project input
zxbcdt = self.in_proj(current_input)
n_heads = self.args.num_heads
z = zxbcdt[:, :, :self.args.intermediate_size]
xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
dt = zxbcdt[:, :, -(n_heads):]
# Process time steps
dt = mx.reshape(dt, (batch_size, n_heads))
dt = mx.clip(nn.softplus(dt + self.dt_bias), self.args.time_step_min, self.args.time_step_max)
dt = mx.maximum(dt, self.args.time_step_floor)
# Apply convolution
xBC = self.conv1d(xBC, cache=current_cache)
xBC = silu(xBC)
# Split states
x = xBC[:, :, :self.args.intermediate_size]
B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
C = xBC[:, :, -self.args.state_size:]
# Reshape for SSM
x = mx.reshape(x, (batch_size, 1, n_heads, self.args.head_dim))
x = mx.squeeze(x, axis=1)
B = mx.reshape(B, (batch_size, 1, self.args.state_size))
B = mx.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
B = mx.expand_dims(B, axis=2)
C = mx.reshape(C, (batch_size, 1, self.args.state_size))
C = mx.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
C = mx.expand_dims(C, axis=3)
# SSM updates
dA = mx.exp(dt * mx.expand_dims(A, 0))
dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
# Update state
x = mx.expand_dims(x, axis=3)
dBx = mx.matmul(x, B)
current_cache[1] = current_cache[1] * dA + dBx
# Compute output
y = mx.matmul(current_cache[1], C)
y = mx.squeeze(y, axis=-1)
y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
y = mx.reshape(y, (batch_size, 1, n_heads * self.args.head_dim))
y = self.norm(y + z)
outputs.append(self.out_proj(y))
# Concatenate all outputs
return mx.concatenate(outputs, axis=1)
class ResidualBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.residual_in_fp32 = args.residual_in_fp32
self.mixer = Mamba2Block(args)
self.norm = nn.RMSNorm(args.hidden_size)
def __call__(self, x: mx.array, cache):
if self.residual_in_fp32:
x = x.astype(mx.float32)
normed = self.norm(x)
output = self.mixer(normed, cache)
return output + x
class Mamba2(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
self.layers = [ResidualBlock(args) for _ in range(args.num_hidden_layers)]
self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
def __call__(self, x: mx.array, cache):
x = self.embeddings(x)
if cache is None:
cache = [None] * len(self.layers)
hidden = x
for layer, c in zip(self.layers, cache):
hidden = layer(hidden, c)
return self.norm_f(hidden)
class Model(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.model_type = args.model_type
self.backbone = Mamba2(args)
if not args.tie_word_embeddings:
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
def __call__(self, inputs: mx.array, cache=None):
hidden = self.backbone(inputs, cache)
if self.args.tie_word_embeddings:
logits = self.backbone.embeddings.as_linear(hidden)
else:
logits = self.lm_head(hidden)
return logits
def make_cache(self):
return [MambaCache() for _ in range(len(self.layers))]
@property
def layers(self):
return self.backbone.layers